Clustering Feature Tree Research Articles

Reducing the time needed to solve a traveling salesman problem by clustering with a Hierarchy-based algorithm

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In this study, we compare a cluster-based whale optimization algorithm (WOA) with an uncombined method to find a more optimized solution for a traveling salesman problem (TSP). The main goal is to reduce the time of solving a TSP. First, we solve the TSP with the Whale optimization algorithm, later we solve it with the combined method of solving TSP which uses the clustering method, called BIRCH (balanced iterative reducing and clustering using hierarchies). Birch builds a clustering feature (CF) tree and then applies one of the clustering methods (for ex. K-means) to cluster data. Experiments performed on three datasets show that the convergence time improves by using the combined algorithm.</p></div></div></div>

Analysis of Folklore Sports Tourism Resources Development and Protection Strategies under the Management of Rural Revitalization Strategy in the Background of Big Data

Abstract Under the background of big data, how to better promote the development and protection of folklore sports tourism resources under the management of rural revitalization strategy is the important research direction of this paper. This paper is based on the data mining method of clustering analysis under big data technology, which is realized by constructing a clustering feature tree, i.e., a CF tree. Next, formal clustering under unguided data mining is introduced. Combined with the folk sports tourism market in Langxia town, the development of folk sports tourism in Langxia town under the management of rural revitalization strategy is analyzed to lay the foundation for further folk sports tourism resources development and protection strategies. The analysis results show that 45.8% of the tourists participating in rural folk sports tourism are male and 54.2% are female; in terms of age composition, tourists aged 18-59 account for 93.5% of the total; in terms of income level, the middle and high-end tourist population accounts for 32%; in terms of education, 65% of the tourists’ cultural level is undergraduate or specialist. Regarding the characteristics of tourists’ travel behavior, the mode of travel is mainly self-driving; the largest proportion of tourists choose to travel with their families, and the per capita consumption expenditure is below 200 yuan. Under the management of the rural revitalization strategy, the main target groups of folklore characteristic sports tourist attractions should be women, middle-aged and middle-aged people, with higher education levels, and higher incomes.

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision.

BETULA: Fast clustering of large data with improved BIRCH CF-Trees

BIRCH clustering is a widely known approach for clustering that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compressed representation of the input data. As new data arrives, the tree is eventually rebuilt to increase the compression. Afterward, the leaves of the tree are used for clustering. Because of the data compression, this method is very scalable. The idea has been adopted, for example, for k-means, data stream, and density-based clustering. Clustering features used by BIRCH are simple summary statistics that can easily be updated with new data: the number of points, the linear sums, and the sum of squared values. Unfortunately, how the sum of squares is then used in BIRCH is prone to catastrophic cancellation. We introduce a replacement cluster feature that does not have this numeric problem, that is not much more expensive to maintain, and which makes many computations simpler and, hence, more efficient. These cluster features can also easily be used in other work derived from BIRCH such as algorithms for streaming data. In the experiments, we demonstrate the numerical problem and compare the performance of the original algorithm compared to the improved cluster features. We furthermore explain how to improve clustering with different algorithms such as hierarchical clustering, k-means, k-means++ and Gaussian mixture modeling by using the variance information stored in the cluster features to obtain high-quality result approximations in a substantially reduced runtime (up to 500× faster in our experiments).

Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation

Currently, medical image segmentation has attracted more attention from researchers, which can assist in medical diagnosis. However, in the process of traditional medical image segmentation, it is sensitive to the initial contour and noise, which is difficult to deal with the weak edge image, complex iterative process. In this paper, we propose a new medical image segmentation method, which adopts density-oriented BIRCH (balanced iterative reducing and clustering using hierarchies) clustering method to modify active contour model and improve the robustness of noise. The BIRCH is a multi-stage clustering method using clustering feature tree. The improved model can effectively deal with the gray non-uniformity of real medical images. And we also introduce a new energy function in active contour model to make the contour curve approach to the edge, and finally stay at the edge of the image to complete the object segmentation. Experimental results show that this new model can overcome the influence of complex background on medical image segmentation and improve the speed and accuracy of medical segmentation results.

Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.

A data cleaning method for water quality based on improved hierarchical clustering algorithm

Identifying and rectifying incomplete water quality data is of vital importance. A data cleaning method based on improved balanced iterative reducing and clustering using hierarchies (BIRCH) clustering algorithm is proposed. The clustering feature tree of water quality data is constructed and the cluster vector of the clustering feature tree is obtained by the agglomerative method. The optimal cluster number is determined according to the Bayesian Information Criterion and the nearest clustering ratio. The Pauta criterion is used to detect the global outlier and artificial neural network (ANN) is used to fill in outliers and missing values. Finally, the improved data cleaning method is applied to water quality monitoring data of Beijing wastewater treatment plant. The experimental results show that the data cleaning method can not only detect abnormal values and missing values accurately, but also normalise and complete missing data.

Local learning integrating global structure for large scale semi-supervised classification

In recent years, semi-supervised learning algorithms have aroused considerable interests from machine learning fields because unlabeled samples are often readily available and labeled ones are expensive to obtain. Graph-based semi-supervised learning has been one of the most active research areas. However, how to speed up these methods for handling large scale datasets is still a challenge. In this paper, we apply the clustering feature tree to large scale graph-based semi-supervised learning and propose a local learning integrating global structure algorithm. By organizing the unlabeled samples with a clustering feature tree, it allows us to decompose the unlabeled samples to a series of clusters (sub-trees) and learn them locally. In each training process on sub-trees, the clustering centers are chosen as frame points to keep the global structure of input samples, and propagate their labels to unlabeled data. We compare our method with several existing large scale algorithms on real-world datasets. The experiments show the scalability and accuracy improvement of our proposed approach. It can also handle millions of samples efficiently.

Privacy-Preserving Hierarchical-k-Means Clustering on Horizontally Partitioned Data

Privacy preserving mining of distributed data is an important direction for data mining, and privacy preserving clustering is one of the main researches. Privacy-preserving data mining techniques enable knowledge discovery without requiring disclosure of private data. The existing privacy preserving algorithms mainly concentrated on association rules and classification, only few algorithms on privacy preserving clustering, and these algorithms mainly concentrated on centralized and vertically partitioned data. So we proposed privacy preserving hierarchical k-means clustering algorithm on horizontally partitioned data, denoted as HPPHKC. The complexity on k-means clustering algorithm is only O(n), so most existing privacy preserving clustering algorithms are concentrated on k-means and based on two parties and the trusted third party, these algorithms have the drawbacks of inaccurate results because of choosing initial clustering centers randomly and applying to multi-party difficult and revealing privacy because of depending on the third party excessively. By introduction of three protocols for secure multi-party computation: distance computation, cluster center computation, and standardization and combination of the merits of hierarchical and k-means clustering, we presented a privacy-preserving hierarchical-k-means clustering algorithm on horizontally partitioned data for semi-honest parties using some secure multi-party computation protocols. The algorithm uses the security protocol mentioned above to achieve the protection of the privacy data, and uses the hierarchical clustering algorithm to obtain k cluster centers, then uses the k-means clustering algorithm to obtain the final k clusters. We introduce the clustering feature and the clustering feature tree, which are used to summarize the cluster representations. A clustering feature (CF) is a three-dimensional vector summarizing information about clusters of objects. The ith clustering feature is CFi = (cni,cci,cpi), where cni is the number of th clusters, denoted as the size of th cluster, cci is the center of the cni objects, and cpi is the pointer of the list of cni objects. The algorithm has two phases: the first phase, every object can be as a cluster, a secure computation protocol is used to compute the dissimilarity matrix and the most similar clusters will be merged. This process is repeated until we get the assigned clusters number k and get k clustering centers. In the second phase, the semi-honest third party and all data involved parties use the k-means algorithm refine the results of the first phase and get the final clustering results. Finally, we give the proof of security of the algorithm and analysis of communication costs, and we show that our scheme is secure and complete with good efficiency.

Chameleon based on clustering feature tree and its application in customer segmentation

Clustering analysis plays an important role in the filed of data mining. Nowadays, hierarchical clustering technique is becoming one of the most widely used clustering techniques. However, for most algorithms of hierarchical clustering technique, the requirements of high execution efficiency and high accuracy of clustering result cannot be met at the same time. After analyzing the advantages and disadvantages of the hierarchical algorithms, the paper puts forward a two-stage clustering algorithm, named Chameleon Based on Clustering Feature Tree (CBCFT), which hybridizes the Clustering Tree of algorithm BIRCH with algorithm CHAMELEON. By calculating the time complexity of CBCFT, the paper argues that the time complexity of CBCFT increases linearly with the number of data. By experimenting on sample data set, this paper demonstrates that CBCFT is able to identify clusters with large variance in size and shape and is robust to outliers. Moreover, the result of CBCFT is as similar as that of CHAMELEON, but CBCFT overcomes the shortcoming of the low execution efficiency of CHAMELEON. Although the execution time of CBCFT is longer than BIRCH, the clustering result of CBCFT is much satisfactory than that of BIRCH. Finally, through a case of customer segmentation of Chinese Petroleum Corp. HUBEI branch; the paper demonstrates that the clustering result of the case is meaningful and useful.

Fast Clustering for Time-series Data with Average-time-sequence-vector Generation Based on Dynamic Time Warping.

This paper proposes a fast clustering method for time-series data based on average time sequence vector. A clustering procedure based on an exhaustive search method is time-consuming although its result typically exhibits high quality. BIRCH, which reduces the number of examples by data squashing based on a data structure CF (Clustering Feature) tree, represents an effective solution for such a method when the data set consists of numerical attributes only. For time-series data, however, a straightforward application of BIRCH based on a Euclidean distance for a pair of sequences, miserably fails since such a distance typically differs from human's perception. A dissimilarity measure based on DTW (Dynamic Time Warping) is desirable, but to the best of our knowledge no methods have been proposed for time-series data in the context of data squashing. In order to circumvent this problem, we propose DTWS (Dynamic Time Warping Squashed) tree, which employs a dissimilarity measure based on DTW, and compresses time sequences to the average time sequence vector. An average time sequence vector is obtained by a novel procedure which estimates correct shrinkage of a result of DTW. Experiments using the Australian sign language data demonstrate the superiority of the proposed method in terms of correctness of clustering, while its degradation of time efficiency is negligible.